BF-2: Quantifying carbon storage
1. Introduction
“Peat soils hold an estimated 650 billion tonnes (Gt = Pg) of carbon on only 3 percent of the Earth’s land surface – a carbon store that is equal in magnitude to the amount of carbon in the Earth’s vegetation, and more than half of the carbon in the atmosphere (Yu et al. 2010, Page et al. 2011, Dargie et al. 2017)….” FAO-UN (2020).
Spatial variability in carbon stored in peatlands complicates upscaling to regional estimates. For instance “regions characterized by warmer and wetter conditions stored the most C as peat” (Packalen et al. 2016). Combining field measurements, satellite data, hydrological and climate data is essential to get better spatially-explicit estimates and reduce uncertainty (seeSothe et al. 2022, Balogun et al. 2023). Similarly, implemeting soil carbon simulation models and identifying areas of improvement would reduce uncertainty in carbon dynamics under climate change (Varney et al. 2022).
2. Three approaches for quantifying carbon storage
Here we present three general approaches for quantifying carbon storage for ecosystem services assessments and show how these three approaches could be adapted to account for peatland carbon dynamics. All these approaches rely on local site-specific field surveys for calibration and validation.
Stratify and multiply
The approach relies on first stratifying or partitioning the study area into multiple unique land cover or ecosystem types (Goetz et al. 2009, Sharp et al. 2020, Martínez-López et al. 2019). A reference or average carbon stock value is assigned to each cover type. The area of each cover type is then multiplied by the cover type’s associated reference carbon stock value.
Many ecosystem service modeling platforms use a Stratify & Multiply approach, including the ARtificial Intelligence for Ecosystem Services, ARIES (Martínez-López et al. 2019) and the Integrated Valuation of Ecosystem Services and Tradeoffs, InVEST (Natural Capital Project, 2023). Best practices include using local site-specific field surveys to derive reference carbon stock values and detailed site-specific land cover maps that account for environmental gradients that might influence carbon stock changes (e.g., climate, soil characteristics, species composition, management, degradation, elevation, site history, wetland type, ecozone, etc.).
Carbon emissions can be assessed in a similar way. For example, maps of peatland types can be combined with estimates of Net Ecosystem Exchange and Methane emission rates compiled for each peatland type and region (Webster et al. 2018).
Assessment of Status
Some Global and Canadian examples of model implementation.
| Models/Approaches | Have they been used? | In Canada? |
|---|---|---|
| Global Land Surface Model is designed to identify the distribution of permafrost. | Yokohata et al. (2020) |
3. Partners working on this topic
| People/Organization/Institution | Topic |
|---|---|
| University of Waterloo (CanPeat) | Canadian Model for Peatlands (CaMP) Can-Peat: Canada’s peatlands as nature-based climate solutions |
| ECCC (Kelly Bona) | |
| WWF | Mapping |
| WCS | Mapping |
| University of Toronto (Sarah Finkelstein) | |
| University of Leads | PEATMAP |
| Stockholm University (Bolin Centre for Climate Research) |
4. Recommendations
Recommendations
There is a need to optimize monitoring/sampling in the region by identifying priority sites where data is needed to improve model performance and reduce uncertainty.
Partnerships with local NGOs, universities/researchers, and indigenous communities are needed to support the implementation of an observing system.
5. Datasets
In construction!!!
| Description | Repository and Layers | Extent, Format, Resolution, Projection |
Reference |
|---|---|---|---|
| Maps of northern peatland extent, depth, carbon storage and nitrogen storage | Stockholm University (Bolin Centre for Cliamte Research) Histel_fraction Histel_minerotrophic Histel_ombrotrophic Histel_SOC_hg_per_sqm Histel_TN_gram_per_sqm Histosol_fraction Histosol_minerotrophic Histosol_ombrotrophic Histosol_SOC_hg_per_sqm Histosol_TN_gram_per_sqm Mean_potential_peat_depth_cm |
Norhtern hemisphere, GeoTiff, 10 Km, World Azimuthal Equidistant projection, |
Hugelius et al. (2020) |
| A map of global peatland extent created using machine learning (Peat-ML) |
Peatland fractional coverage |
Global, NetCDF (Network Common Data Form), 0.0833 (~1Km), lonlat |
Melton et al. (2022) |
| PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis | Xu et al. (2018) | ||
Example
Peat ML global peatland_extent from Melton et al. (2022)
Peat cores Hugelius et al. (2020)